Microsoft Technology Licensing, LLC

Sorin Iftimie

Thomas Moscibroda

Karthikeyan Subramanian

Yong Xu

Ajay Mani

Qingwei Lin

Si Qin

Shandan Zhou

Saurabh Agarwal

Nisarg Tarakkumar Sheth

Sandeep Ramji

Paul Naveen Selvaraj

Wanghai Gu

Yunus Mohammed

Gurpreet Virdi

Sanjay Ramanujan

Bruce P Bequette

Ikrima Elhassan

Fernando Gonzalez Todisco

Vi Lam Nguyen

Rostislav Sudakov

Xing Wen Wang

The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Predicting expansion failures and defragmenting cluster resources

A cloud computing capacity management system can include a fine-grained admission control layer, a policy engine, and an enforcement layer. The fine-grained admission control layer can be configured to ingest capacity signals and create a capacity mitigation policy, based at least in part on the capacity signals, to protect available capacity of a cloud computing system for prioritized users. The capacity mitigation policy can be directed to users of the cloud computing system. The policy engine can be configured to control how the capacity mitigation policy is applied to the cloud computing system. The enforcement layer can be configured to handle incoming resource requests and to enforce resource limits based on the capacity mitigation policy as applied by the policy engine.

Cloud computing capacity management system using automated fine-grained admission control

A self-service system and method for credential reset permits an administrator to customize policies for credential reset based on any user or group of users. Administrators may choose to set a more stringent policy for credential reset for users or groups that have higher-level permissions to access sensitive information within the resource protected by the credential. Customizable, plug-in gates are provided to permit administrators fine grained control over reset policy definition. When the user initiates a credential reset, the reset policy applicable to that user is invoked, and the user is presented with gates to pass pursuant to the applicable reset policy. The user's responses are compared to responses presented by the user at registration. If the responses meet the reset policy's threshold for accuracy, the user is permitted to reset the credential.

Self-service credential management

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Sammamish, WA, United States of America

Sorin Iftimie